The Design of Experiment as a Tool to Model Plant Trace-Metal Bioindication Abilities

Bioindicator plants are species that have the capacity to linearly uptake some elements (metal and metalloids) from the growing substrate, thus reflecting their concentration in the soil. Many factors can influence the uptake of these elements by plants, among which is the simultaneous presence of several metals, a common situation in contaminated or natural soils. A novel approach that can be used to validate the bioindication ability of a species growing on a polymetallic substrate is the design of experiment (DoE) approach. The aim of the present study was to apply the DoE in full factorial mode to model the Cu, Cd, Pb, Zn, and Cr bioindication capacity of Polygonum aviculare, used as the model plant. The results showed that P. aviculare has the ability to bioindicate Cd and Cr with a linear uptake (from 0.35 to 6.66, and 0.1 to 3.4 mg kg⁻¹, respectively) unaffected by the presence of other metals. Conversely, the uptake of Pb, Cu, and Zn is strongly influenced by the presence of all the studied metals, making their concentration in the plant shoot not proportional to that of the soil. In conclusion, these preliminary results confirmed that the DoE can be used to predict the bioindicator abilities of a plant for several elements at the same time and to evaluate the interactions that can be established between variables in the growing medium and in the plant itself. However, more studies including other plant species are needed to confirm the effectiveness of this method.     

Integration of high speed microwave chemistry and a statistical ‘design of experiment’ approach for the synthesis of the mitotic kinesin Eg5 inhibitor monastrol

The rapid preparation of the mitotic kinesin Eg5 inhibitor monastrol has been performed using multicomponent chemistry and combining microwave-assisted synthesis and a statistical design of experiments (DoEs) approach. By variation of the solvent, catalyst type and concentration, reaction time, and temperature a matrix of experiments for the DoE method was derived. As a result of 29 experiments with reaction times ranging from 10 to 30 min, an optimized procedure was derived that allowed the preparation of monastrol in 82%.     

Application of Experimental Design Methodologies in the Enantioseparation of Pharmaceuticals by Capillary Electrophoresis: A Review

Chirality is one of the major issues in pharmaceutical research and industry. Capillary electrophoresis (CE) is an interesting alternative to the more frequently used chromatographic techniques in the enantioseparation of pharmaceuticals, and is used for the determination of enantiomeric ratio, enantiomeric purity, and in pharmacokinetic studies. Traditionally, optimization of CE methods is performed using a univariate one factor at a time (OFAT) approach; however, this strategy does not allow for the evaluation of interactions between experimental factors, which may result in ineffective method development and optimization. In the last two decades, Design of Experiments (DoE) has been frequently employed to better understand the multidimensional effects and interactions of the input factors on the output responses of analytical CE methods. DoE can be divided into two types: screening and optimization designs. Furthermore, using Quality by Design (QbD) methodology to develop CE-based enantioselective techniques is becoming increasingly popular. The review presents the current use of DoE methodologies in CE-based enantioresolution method development and provides an overview of DoE applications in the optimization and validation of CE enantioselective procedures in the last 25 years. Moreover, a critical perspective on how different DoE strategies can aid in the optimization of enantioseparation procedures is presented.     

Identification and removal of sulfhydryl groups from wastewaters

Thiols represent a source of environmental pollution especially wastewater. This work proposed to study the elaboration of cellulose acetate polymer-based membranes for their application in the removal of a sulfhydryl groups of real biological treated wastewaters. The addition of nanoparticles to membranes improves the water purification capacity by promoting a good separation of sulfur, more particularly by ZnO-NPs. We used ultrafiltration membrane-assisted ZnO and TiO₂ NPs application on real effluents from different biological treatment plants. We identified the hydrosulfite (thiol) group in wastewater and we used membrane processes ultrafiltration technique for sulfur removal. We evaluated the degradation of sulfur in biological treatment plants in Tunisia: The urban wastewater treatment plant or the conventional plant of Rades Malienne is a secondary biological wastewater treatment plant noted STEP1. The rural wastewater treatment plant based vertical flow planted with Phragmites australis from the Grombalia region noted STEP2 and rural wastewater treatment plant based horizontal flow planted with Phragmites australis from the Grombalia region noted STEP3. STEP1 is found to be more loaded with sulfur. Application of AC-ZnO membrane gives 99.07% and 99.55% of sulfur removal from wastewater of STEP1 and STEP3. STEP3 is 50 times less charged on sulfur than STEP1. We suggested that when the sulfur content is high, this leads to an increase in mineral elements. This could be explained by the interactions between thiols and the major elements that cause mineral pollution.     

Statistical experimental design for bioprocess modeling and optimization analysis

The statistical design of experiments (DOE) is a collection of predetermined settings of the process variables of interest, which provides an efficient procedure for planning experiments. Experiments on biological processes typically produce long sequences of successive observations on each experimental unit (plant, animal, bioreactor, fermenter, or flask) in response to several treatments (combination of factors). Cell culture and other biotech-related experiments used to be performed by repeated-measures method of experimental design coupled with different levels of several process factors to investigate dynamic biological process. Data collected from this design can be analyzed by several kinds of general linear model (GLM) statistical methods such as multivariate analysis of variance (MANOVA), univariate ANOVA (timesplit-plot analysis with randomization restriction), and analysis of orthogonal polynomial contrasts of repeated factor (linear coefficient analysis). Last, regression model was introduced to describe responses over time to the different treatments along with model residual analysis. Statistical analysis of biprocess with repeated measurements can help investigate environmental factors and effects affecting physiological and bioprocesses in analyzing and optimizing biotechnology production.     

Statistical experimental design for bioprocess modeling and optimization analysis: repeated-measures method for dynamic biotechnology process

The statistical design of experiments (DOE) is a collection of predetermined settings of the process variables of interest, which provides an efficient procedure for planning experiments. Experiments on biological processes typically produce long sequences of successive observations on each experimental unit (plant, animal, bioreactor, fermenter, or flask) in response to several treatments (combination of factors). Cell culture and other biotech-related experiments used to be performed by repeated-measures method of experimental design coupled with different levels of several process factors to investigate dynamic biological process. Data collected from this design can be analyzed by several kinds of general linear model (GLM) statistical methods such as multivariate analysis of variance (MANOVA), univariate ANOVA (time split-plot analysis with randomization restriction), and analysis of orthogonal polynomial contrasts of repeated factor (linear coefficient analysis). Last, regression model was introduced to describe responses over time to the different treatments along with model residual analysis. Statistical analysis of biprocess with repeated measurements can help investigate environmental factors and effects affecting physiological and bioprocesses in analyzing and optimizing biotechnology production.     

Characterising and correcting batch variation in an automated direct infusion mass spectrometry (DIMS) metabolomics workflow

Direct infusion mass spectrometry (DIMS)-based untargeted metabolomics measures many hundreds of metabolites in a single experiment. While every effort is made to reduce within-experiment analytical variation in untargeted metabolomics, unavoidable sources of measurement error are introduced. This is particularly true for large-scale multi-batch experiments, necessitating the development of robust workflows that minimise batch-to-batch variation. Here, we conducted a purpose-designed, eight-batch DIMS metabolomics study using nanoelectrospray (nESI) Fourier transform ion cyclotron resonance mass spectrometric analyses of mammalian heart extracts. First, we characterised the intrinsic analytical variation of this approach to determine whether our existing workflows are fit for purpose when applied to a multi-batch investigation. Batch-to-batch variation was readily observed across the 7-day experiment, both in terms of its absolute measurement using quality control (QC) and biological replicate samples, as well as its adverse impact on our ability to discover significant metabolic information within the data. Subsequently, we developed and implemented a computational workflow that includes total-ion-current filtering, QC-robust spline batch correction and spectral cleaning, and provide conclusive evidence that this workflow reduces analytical variation and increases the proportion of significant peaks. We report an overall analytical precision of 15.9 %, measured as the median relative standard deviation (RSD) for the technical replicates of the biological samples, across eight batches and 7 days of measurements. When compared against the FDA guidelines for biomarker studies, which specify an RSD of <20 % as an acceptable level of precision, we conclude that our new workflows are fit for purpose for large-scale, high-throughput nESI DIMS metabolomics studies.     

Innovative high-performance liquid chromatography method development for the screening of 19 antimalarial drugs based on a generic approach, using design of experiments, independent component analysis and design space

An innovative methodology based on design of experiments (DoE), independent component analysis (ICA) and design space (DS) was developed in previous works and was tested out with a mixture of 19 antimalarial drugs. This global LC method development methodology (i.e. DoE-ICA-DS) was used to optimize the separation of 19 antimalarial drugs to obtain a screening method. DoE-ICA-DS methodology is fully compliant with the current trend of quality by design. DoE was used to define the set of experiments to model the retention times at the beginning, the apex and the end of each peak. Furthermore, ICA was used to numerically separate coeluting peaks and estimate their unbiased retention times. Gradient time, temperature and pH were selected as the factors of a full factorial design. These retention times were modelled by stepwise multiple linear regressions. A recently introduced critical quality attribute, namely the separation criterion (S), was also used to assess the quality of separations rather than using the resolution. Furthermore, the resulting mathematical models were also studied from a chromatographic point of view to understand and investigate the chromatographic behaviour of each compound. Good adequacies were found between the mathematical models and the expected chromatographic behaviours predicted by chromatographic theory. Finally, focusing at quality risk management, the DS was computed as the multidimensional subspace where the probability for the separation criterion to lie in acceptance limits was higher than a defined quality level. The DS was computed propagating the prediction error from the modelled responses to the quality criterion using Monte Carlo simulations. DoE-ICA-DS allowed encountering optimal operating conditions to obtain a robust screening method for the 19 considered antimalarial drugs in the framework of the fight against counterfeit medicines. Moreover and only on the basis of the same data set, a dedicated method for the determination of three antimalarial compounds in a pharmaceutical formulation was optimized to demonstrate both the efficiency and flexibility of the methodology proposed in the present study.     

Covariances Simultaneous Component Analysis: a new method within a framework for modeling covariances

In modern omics research, it is more rule than exception that multiple data sets are collected in a study pertaining to the same biological organism. In such cases, it is worthwhile to analyze all data tables simultaneously to arrive at global information of the biological system. This is the area of data fusion or multi‐set analysis, which is a lively research topic in chemometrics, bioinformatics, and biostatistics. Most methods of analyzing such complex data focus on group means, treatment effects, or time courses. There is also information present in the covariances among variables within a group, because this relates directly to individual differences, heterogeneity of responses, and changes of regulation in the biological system. We present a framework for analyzing covariance matrices and a new method that fits nicely in this framework. This new method is based on combining covariance prototypes using simultaneous components and is, therefore, coined Covariances Simultaneous Component Analysis (COVSCA). We present the framework and our new method in mathematical terms, thereby explaining the (dis)similarities of the methods. Systems biology models based on differential equations illustrate the type of variation generated in real‐life biological systems and how this type of variation can be modeled within the framework and with COVSCA. The method is subsequently applied to two real‐life data sets from human and plant metabolomics studies showing biologically meaningful results. Copyright © 2015 John Wiley & Sons, Ltd.     

Serious complications in experiments in which UV doses are effected by using different lamp heights

Many experiments examining plant responses to enhanced ultraviolet-B radiation (280–315 nm) simply compare an enhanced UV-B treatment with ambient UV-B (or no UV-B radiation in most greenhouse and controlled-environment studies). Some more detailed experiments utilize multiple levels of UV-B radiation. A number of different techniques have been used to adjust the UV dose. One common technique is to place racks of fluorescent UV-emitting lamps at different heights above the plant canopy. However, the lamps and associated support structure cast shadows on the plant bed below. We calculated one example of the sequence of shade intervals for two common heights of lamp racks and show the patterns and duration of shade which the plants receive is distributed differently over the course of the day for different heights of the lamp racks. We also conducted a greenhouse experiment with plants (canola, sunflower and maize) grown under unenergized lamp racks suspended at the same two heights above the canopy. Growth characteristics differed in unpredictable ways between plants grown under the two heights of lamp racks. These differences could enhance or obscure potential UV-B effects. Also, differences in leaf mass per unit foliage area, which were observed in this experiment, could contribute to differences in plant UV-B sensitivity. We recommend the use of other techniques for achieving multiple doses of UV-B radiation. These range from simple and inexpensive approaches (e.g., wrapping individual fluorescent tubes in layers of a neutral-density filter such as cheese cloth) to more technical and expensive alternatives (e.g., electronically modulated lamp control systems). These choices should be determined according to the goals of the particular experiment.     

Novel method for constructing a large-scale design space in lubrication process by using bayesian estimation based on the reliability of a scale-up rule

A reliable large-scale design space was constructed by integrating the reliability of a scale-up rule into the Bayesian estimation without enforcing a large-scale design of experiments (DoE). A small-scale DoE was conducted using various Froude numbers (X(1)) and blending times (X(2)) in the lubricant blending process for theophylline tablets. The response surfaces, design space, and their reliability of the compression rate of the powder mixture (Y(1)), tablet hardness (Y(2)), and dissolution rate (Y(3)) on a small scale were calculated using multivariate spline interpolation, a bootstrap resampling technique, and self-organizing map clustering. A constant Froude number was applied as a scale-up rule. Experiments were conducted at four different small scales with the same Froude number and blending time in order to determine the discrepancies in the response variables between the scales so as to indicate the reliability of the scale-up rule. Three experiments under an optimal condition and two experiments under other conditions were performed on a large scale. The response surfaces on the small scale were corrected to those on the large scale by Bayesian estimation using the large-scale results and the reliability of the scale-up rule. Large-scale experiments performed under three additional sets of conditions showed that the corrected design space was more reliable than the small-scale design space even when there was some discrepancy in the pharmaceutical quality between the manufacturing scales. This approach is useful for setting up a design space in pharmaceutical development when a DoE cannot be performed at a commercial large manufacturing scale.     

Understanding nanoparticle cellular entry: A physicochemical perspective

Understanding interactions between nanoparticles (NPs) with biological matter, particularly cells, is becoming increasingly important due to their growing application in medicine and materials, and consequent biological and environmental exposure. For NPs to be utilised to their full potential, it is important to correlate their functional characteristics with their physical properties, which may also be used to predict any adverse cellular responses. A key mechanism for NPs to impart toxicity is to gain cellular entry directly. Many parameters affect the behaviour of nanomaterials in a cellular environment particularly their interactions with cell membranes, including their size, shape and surface chemistry as well as factors such as the cell type, location and external environment (e.g. other surrounding materials, temperature, pH and pressure). Aside from in vitro and in vivo experiments, model cell membrane systems have been used in both computer simulations and physicochemical experiments to elucidate the mechanisms for NP cellular entry. Here we present a brief overview of the effects of NPs physical parameters on their cellular uptake, with focuses on 1) related research using model membrane systems and physicochemical methodologies; and 2) proposed physical mechanisms for NP cellular entrance, with implications to their nanotoxicity. We conclude with a suggestion that the energetic process of NP cellular entry can be evaluated by studying the effects of NPs on lipid mesophase transitions, as the molecular deformations and thus the elastic energy cost are analogous between such transitions and endocytosis. This presents an opportunity for contributions to understanding nanotoxicity from a physicochemical perspective.     

Integration of gas chromatography mass spectrometry methods for differentiating ricin preparation methods

The investigation of crimes involving chemical or biological agents is infrequent, but presents unique analytical challenges. The protein toxin ricin is encountered more frequently than other agents and is found in the seeds of Ricinus communis, commonly known as the castor plant. Typically, the toxin is extracted from castor seeds utilizing a variety of different recipes that result in varying purity of the toxin. Moreover, these various purification steps can also leave or differentially remove a variety of exogenous and endogenous residual components with the toxin that may indicate the type and number of purification steps involved. We have applied three gas chromatography-mass spectrometry (GC-MS) based analytical methods to measure the variation in seed carbohydrates and castor oil ricinoleic acid, as well as the presence of solvents used for purification. These methods were applied to the same samples prepared using four previously identified toxin preparation methods, starting from four varieties of castor seeds. The individual data sets for seed carbohydrate profiles, ricinoleic acid, or acetone amount each provided information capable of differentiating different types of toxin preparations across seed types. However, the integration of the data sets using multivariate factor analysis provided a clear distinction of all samples based on the preparation method, independent of the seed source. In particular, the abundance of mannose, arabinose, fucose, ricinoleic acid, and acetone were shown to be important differentiating factors. These complementary tools provide a more confident determination of the method of toxin preparation than would be possible using a single analytical method.     

Evaluation of different machines used to quantify genetic modification by real-time PCR

Quantification of genetic modification (GM) is often undertaken to test for compliance with the European Union GM labeling threshold in food. Different control laboratories will often use common validated methods, but with different models of real-time PCR machines. We performed two separate ring trials to evaluate the relative precision and accuracy of different types of real-time PCR machines used to quantify the concentration of GM maize. Both trials used dual-labeled fluorogenic probes for quantification. The first ring trial used separate GM and reference assays (a single fluorescence channel), and the second used a combined duplex assay (two simultaneous fluorescence channels). Five manufacturers and seven models--including a 96-well microtiter-plate, rotary, and portable machines--were examined. In one trial, the machine used had a significant effect on precision, but in the other it did not. Overall, the degree of variation due to the machine model was lower than other factors. No significant repeatable difference in accuracy was observed between machine models. It was not possible to use sufficient replication of machine type in each laboratory to examine all sources of variation in this study, but the results strongly indicate that factors other than machine type or manufacturer (e.g., method or laboratory) contribute more to variation in a GM quantification result.     

Nanosensors in environmental analysis

Nanoscience and nanotechnology deal with the study and application of structures of matter of at least one dimension of the order of less than 100 nm (1 nm = one millionth of a millimetre). However, properties related to low dimensions are more important than size. Nanotechnology is based on the fact that some very small structures usually have new properties and behaviour that are not displayed by the bulk matter with the same composition.This overview introduces and discusses the main concepts behind the development of nanosensors and the most relevant applications in the field of environmental analysis. We focus on the effects (many of which are related to the quantum nature) that distinguish nanosensors and give them their particular behaviour. We will review the main types of nanosensors developed to date and highlight the relationship between the property monitored and the type of nanomaterial used.We discuss several nanostructures that are currently used in the development of nanosensors: nanoparticles, nanotubes, nanorods, embedded nanostructures, porous silicon, and self-assembled materials. In each section, we first describe the type of nanomaterial used and explain the properties related to the nanostructure. We then briefly describe the experimental set up and discuss the main advantages and quality parameters of nanosensing devices. Finally, we describe the applications, many of which are in the environmental field.     

Optimization of capillary zone electrophoresis for charge heterogeneity testing of biopharmaceuticals using enhanced method development principles

CZE is a well‐established technique for charge heterogeneity testing of biopharmaceuticals. It is based on the differences between the ratios of net charge and hydrodynamic radius. In an extensive intercompany study, it was recently shown that CZE is very robust and can be easily implemented in labs that did not perform it before. However, individual characteristics of some examined proteins resulted in suboptimal resolution. Therefore, enhanced method development principles were applied here to investigate possibilities for further method optimization. For this purpose, a high number of different method parameters was evaluated with the aim to improve CZE separation. For the relevant parameters, design of experiments (DoE) models were generated and optimized in several ways for different sets of responses like resolution, peak width and number of peaks. In spite of product specific DoE optimization it was found that the resulting combination of optimized parameters did result in significant improvement of separation for 13 out of 16 different antibodies and other molecule formats. These results clearly demonstrate generic applicability of the optimized CZE method. Adaptation to individual molecular properties may sometimes still be required in order to achieve optimal separation but the set screws discussed in this study [mainly pH, identity of the polymer additive (HPC versus HPMC) and the concentrations of additives like acetonitrile, butanolamine and TETA] are expected to significantly reduce the effort for specific optimization.     

Use of statistical design of experiments in the optimization of amide synthesis utilizing polystyrene-supported N-hydroxybenzotriazole resin

Two fields that routinely perform reaction optimization studies are chemical development (prior to scale-up) and medicinal or combinatorial chemistry (prior to analogue synthesis or library production). To date, the use of statistical design of experiments (DoE) in conjunction with automated synthesizers has been applied in process research to a greater extent than in the medicinal or combinatorial laboratories. We have applied DoE in conjunction with an automated synthesizer to optimize the synthesis of amides employing resin-bound N-hydroxybenzotriazole (PS-HOBt) active esters as intermediates. This methodology allowed the rapid development of an improved protocol for the parallel synthesis of amides by conversion of carboxylic acids to PS-HOBt esters followed by treatment with appropriate amines. Product isolation involved only simple filtration and evaporation.     

Applications of nuclear analytical techniques in environmental research. Plenary lecture.

Among nuclear analytical techniques, neutron activation analysis (NAA) is particularly useful for environmental studies. It affords low detection limits for many elements, high specificity and few sources of systematic error, which means that high accuracy is attainable. Neutron activation analysis is particularly useful for trace and ultra-trace analysis of environmental samples (water, soils, rocks and biological material). In trace element work associated with pollution, instrumental NAA is a powerful technique for multi-element surveys, in particular when combined with other spectroscopic techniques. Nuclear techniques, as with most analytical techniques, cannot be used to distinguish between different physico-chemical forms of an element per se. When used in combination with appropriate separation techniques, however, nuclear techniques can provide valuable information about trace element speciation in environmental and biological systems. From dynamic tracer experiments, i.e., addition of chemically well defined labelled compounds to environmental systems, valuable information can be obtained on the distribution of species and on microchemical processes influencing the physico-chemical forms. In these laboratories, speciation studies on trace elements in natural waters have been carried out by using instrumental NAA in combination with physical separation techniques, such as dialysis and ultrafiltration, in situ and in the laboratory. Dynamic radiotracer experiments have provided important information about processes influencing the speciation of trace elements in aquatic systems. Sequential extraction techniques have proved to be useful in studies on sediments and soils when combined with NAA. Sequential extractions also provide significant information about the physico-chemical behaviour of radionuclides supplied to natural soils from the Chernobyl accident.     

Design of experiment techniques for the optimization of chromatographic analysis conditions: A review

Design of experiment (DoE) techniques have been widely used in the field of chromatographic parameters optimization as a valuable tool. A systematic literature review of the available DoE techniques applied to the development of a chromatographic analysis method is presented in this paper. First, the most common available designs and the implementation steps of DoE are comprehensively introduced. Then the studies in recent 10 years for the application of DoE techniques in various chromatographic techniques are discussed, such as capillary electrophoresis, liquid chromatography, gas chromatography, thin-layer chromatography, and high-speed countercurrent chromatography. Current problems and future outlooks are finally given to provide a certain inspiration of research in the application of DoE techniques to the different chromatographic techniques field. This review contributes to a better understanding of the DoE techniques for the efficient optimization of chromatographic analysis conditions, especially for the analysis of complex systems, such as multicomponent drugs and natural products.